Optimization and modeling of ammonia nitrogen removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process

Reza, Arif; Chen, Lide

doi:10.1016/j.scitotenv.2022.158321

Cited by 14 publications

(18 citation statements)

References 78 publications

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“…Table 1 represents sample experimental matrices of aforementioned experimental designs having three variables at three levels. For example, while crafting an experimental design for ammonia removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process factors such as temperature, vacuum pressure, and treatment time, are found to be most critical for efficient process operation [ 33 , 63 ]. The levels of these factors should therefore be selected on the basis of the expected range of operating conditions and available resources.…”

Section: Steps Of Response Surface Methodologymentioning

confidence: 99%

“…When the system was first developed by Ukwuani and Tao [ 131 ], more than 93 % of ammonia was removed from anaerobically digested liquid dairy manure after 180 min of treatment time. Later, Reza and Chen [ 33 ] improved and employed RSM to optimize the treatment process and obtained identical removal efficiency within 88 min of process operation. Similar augmentation in process performance were also reported by researchers for other physical treatment technologies upon optimization using RSM [ 31 , 32 , 132 , 133 ].…”

Section: Application Of Rsm In Various Livestock Wastewater Treatment...mentioning

confidence: 99%

“…Factors such as changes in LWW compositions, full-scale reactor design, equipment, and ambient conditions can impact the actual performance of the optimized process, which may deviate from the predicted performance based on the RSM model. Furthermore, RSM requires a predefined range of input variable values for experimentation, which may not fully represent the wide variability of LWW characteristics and operating conditions [ 33 ]. This can result in suboptimal solutions if the optimal operating conditions lie outside the experimental range [ 11 ].…”

Section: Challenges Associated With Rsm Application In Livestock Wast...mentioning

confidence: 99%

“…Despite the limitations, RSM has successfully been applied to various LWW treatment processes to ensure efficient and effective removal of pollutants from livestock waste streams. For example, RSM has been used to optimize physical treatment processes such as air-recirculated stripping [ 30 ], aqueous ammonia soaking pretreatment [ 31 ], microwave-assisted digestion [ 32 ], microwave-assisted thermal stripping [ 33 ], etc. In the case of chemical processes, RSM has been used to optimize important parameters for electrocoagulation, precipitation, adsorption, pyrolysis, and Fenton processes to accomplish maximum removal of pollutants [ 13 , [34] , [35] , [36] , [37] ].…”

Section: Introductionmentioning

confidence: 99%

“…These studies have demonstrated the effectiveness and versatility of RSM in LWW treatment process optimization and control. RSM, in general, provides a systematic approach for designing experiments, collecting data, and developing mathematical models that can help in understanding the interactions among different process parameters and their effects on treatment performance [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

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Response surface methodology for process optimization in livestock wastewater treatment: A review

Reza,

Chen,

Mao

2024

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Section: Steps Of Response Surface Methodologymentioning

confidence: 99%

Section: Application Of Rsm In Various Livestock Wastewater Treatment...mentioning

confidence: 99%

Section: Challenges Associated With Rsm Application In Livestock Wast...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Response surface methodology for process optimization in livestock wastewater treatment: A review

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Optimization of pollutants removal from anaerobically digested dairy wastewater by electro-oxidation process: a response surface methodology modeling and validation

Das,

Reza,

Chen

2024

J Appl Electrochem

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The release of anaerobically digested dairy wastewater (ANDDW) without a treatment can lead to severe environmental pollution, prompting the exploration of effective and sustainable treatment methods. Amidst various wastewater treatment approaches, the electro-oxidation (EO) process is considered as a promising, clean, and adaptable solution. In this study, the major operational parameters viz. current density, electrolyte concentration, treatment time, and mixing speed of an EO comprising Ti/PbO2 anode and stainless-steel cathode, were optimized using response surface methodology (RSM) for efficient removal of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), orthophosphate (OP), total nitrogen (TN), and total Kjeldahl nitrogen (TKN) from ANDDW. Optimal conditions were identified as a current density of 90 mA cm−2, 0.08% electrolyte concentration, 180 min treatment time, and 400 rpm mixing speed. Under the optimum conditions, the COD, NH3-N, TP, OP, TN, and TKN removal efficiencies were 78.36, 63.93, 87.41, 92.39, 67.01, and 81.42%, respectively. Furthermore, the reaction rate followed the first-order kinetic model for the pollutants removal with correlation coefficients (R2) close to 1. The findings highlight the potential of using the EO process to treat high pollutant-laden ANDDW and encourage further studies to confirm the corresponding outcomes on a pilot scale. Graphical abstract

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Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: kinetics and pathway speculation

Li,

Hu,

Zhao

et al. 2024

Environ Sci Pollut Res

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Overusing triclosan (TCS) endangered ecological safety and human health, and the pandemic of COVID-19 aggravates the accumulation of TCS in the aquatic environment. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. An aerobic bacterium, Bacillus sp. DL4 was isolated with the capability of TCS biodegradation. Response surface methodology (RSM) and arti cial neural network (ANN) were carried out to optimize and verify the different condition variables. All the variables were linear and the interaction of the three factors signi cantly affected TCS removal at the quadratic level (p < 0.001). Under the optimal conditions (35℃, initial pH 7.31, and 5% strain DL4), the TCS removal rate of 95.89 ± 0.68% was observed and found to be consistent with the predicted values from RSM and ANN models. In addition, statistical comparisons between the models indicated that the ANN model had a stronger predictive capability than the RSM model. Kinetic studies showed that TCS degradation was consistent with a pseudo-rst-order kinetic model. Whole genome sequencing indicated that many functional genes were involved in and facilitated TCS degradation. Main metabolite products were detected and identi ed during the biodegradation process by LC-MS, and a possible degradation pathway was tentatively hypothesized. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.

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Optimization and modeling of ammonia nitrogen removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process

Cited by 14 publications

References 78 publications

Response surface methodology for process optimization in livestock wastewater treatment: A review

Response surface methodology for process optimization in livestock wastewater treatment: A review

Optimization of pollutants removal from anaerobically digested dairy wastewater by electro-oxidation process: a response surface methodology modeling and validation

Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: kinetics and pathway speculation

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